Experiments working on that. Uh, so for these signals that are two types.
The first one is that one recall, which, uh, the dark matter kind of book is an electron and nuclear recoil.
Will produce more light and less… more carbon, less heat. A new period code will convert more energy into heat.
So this is the map for S1 and S2, C2 stands for corrected.
Uh, from detective effects and other… And these are, uh, for every particle, we have a pie chart for, uh, probability of.
Of the protocol tape. And, uh, in the detector, we will get, uh.
Many articles from Postmates, from documentary flows and other stuff. So, electron farmers have also produced mostly electron renewals and neutrons on all the dark matter, we call it quickly interactive method particles.
Look for the significant race, boys. Uh, and, um…
For nuclear weapons, it's sensible to, uh, stretch fault when we add.
As, uh, some KUB level. This is the energy for the nuclear ripple.
And, uh, converting to particle math, this approximately several GEV. So, for that, when it was the masses.
Uh, way smaller, so it will extend to K, you've got even several electrical.
So, what do we… how do we detect it? We can only detect it through the middle, in fact.
This is a typical new territory for a low-mass time. Helpful, and it will come into the Xenon medium and hit the Xenon atom.
Nuclei, and, uh, scatter out. So, the energy relation is from here, it's not observable because this is the observable origin from a power detector.
But if, for some, uh… low probability of building impacts, and that one will be left behind by the nuclear reform.
And the electron goes out. So, we can detect the electron as, um.
Kind of similar to electron repos, uh, energy scale. So, it will expand into the optical energy range.
But this effect has never been directly measured by any experiment. There are only indirect, uh.
Evidence. The offense was confined.
Mental in 1939, and then, I think it was one in… set up difference, and they are directly measuring.
A lot of, kind of. Uh, interactions, but never for the nuclear plug.
But then I'm scared for, uh, using mental effects in my head search.
The MGTOW experiment was then done to, uh, productive measure and make no effect.
So we use, uh, two types of infer bins. At SDFC. Uh.
The first one is teaching your complaint for a higher energy, because.
Uh, we can… we have more probability to measure the middle electron.
And the second one is deviating your energy you've seen, uh, mentally.
Experiment, and the documentary that I was search experiments. To simulate signals from WIPS and other kinds of submar particles.
Uh, a central meeting for this kind of, uh. Experiments is on the low pressure.
Therefore, although gas mixture, because, uh, liquid zenith is. Very thin, so it's not possible to see the full track depth of the electron.
And high pressure sample for the same reason. It's not suitable.
So we chose low-pressure sample. For a clear, uh, like, lack.
And, uh, a simple simulation like its wave planets, so it's seen the visible and, like, the UV region.
It can be collected by a normal CMOST detector.
Uh, as this is the setup for the McDonald, uh, uptake or time pressure chamber. So, I'm staying this way.
Produce, uh, you actually, for example, and nuclear ripple signals. So the electron will be… uh, drifted downwards, uh.
Just in the field, and it goes to the gas stems.
And the classroom will amplify the electrics and optics signal, because during the electron avanche.
There are a lot of photons produced. So the fullness will be, uh, collected by both the PMT above and the camera below.
For external, uh, external figure and… And the anti-class electrics at home will be.
Um, we'll be planted by the ITO match. At the bottom, so we can have a…
A signal for an inferior reconstruction and so on.
And this is, uh, a picture of our, uh, timeless exit chamber.
And this is the FAA slide. Original depth.
So, our time is, uh… Very, very thick. This… more than 500 microscope.
And the axon works go all this way, and other launch in jambo.
So we have two or three different gems, and then haven't decided yet.
And, uh. If that trauma experience ablance.
And then another round of Amazon to maximize our game.
There are various kinds of interactions between them. Mostly it's, uh, elastic feathering at its top level, and then…
Several amenities, uh, angular attachments, and other… Other impacts.
But for the gym, how do you stimulate it? We need to first know the geometry, and.
And produce a film map for the gym. So we use, uh, console, some of the simulation software.
And it's used to find a head movement methods to produce the… the field of maths and geometry as a whole.
For the chance, and we just used, uh. To a corner of Gems, and then in Garfield, we can produce mirrors… use mirror symmetry to produce the whole layer on 10.
And as you can see in the Ethiop counter, this is… the comfort of the… it actually feels magnets, you know?
So, at the corner of this house. We have the largest field intensity.
And this will cause problems, Nathan.
So we seem to have a lot of… uh, gems with love.
Different, uh, diameters, uh, from, uh, 150 to 250. And we compare it with, uh.
With the simulated results from a parallel plate with the same energy across.
So they have parallel types. We have a uniform build, a perfect field, but in these, uh.
Gentlemen, so we don't have a video, and they will collide with the glass walls, because.
The mean for a path is several tens of microns. It's comparable to the radius of our gem poles.
And as you can see, in this histogram. This is a histogram for… the endpoints of the electrons.
And that's when we'll, uh… coming this way, and Avalanche in hope. So, a lot of electrons will collide with the passport of the gem.
And, uh, a lot of the atoms will attach to the autumn.
And only about 16 or 17% survived to the analog.
Along with the pain difference, we also discovered a difference in search fields.
Because we… we chose the field. This stands for Drip Region and the action region. For some reason, in the drift region, which costs 200.
Uh, both present and here, because. It will minimize the… the transverse diffusion, and in the induction region, which was 400 for minimization of.
Of longitudinal computation, because we want a sharp signal at the bottom.
Um, but, um, if we open up our genitals, there will be.
Uh, faculty pitching to… into the dripland in Action region, which is mostly. So we need to do some corrections of the fields.
Uh, then we investigated, uh, some possible. Corrections and, uh, the, the… the effect of, for instance, you know, our game.
So this is the voltage… the damn, this voltage code will only set.
So, 170 and 410 microns. These are the… really, the diameters we used in our detector.
And, uh, after looking at Phil's and, uh, being on Netflix, we also.
Simulations instead for, for, uh, optical readouts. So, um, there's no…
Theory or not, uh, current explanation for simulation. So we have to tune some parameters to match the experiment results.
So, um… The results was… the result was… number of, uh, number of functions produced.
Uh, over in the most electrons produced is around 34%. This is a very robust result.
Because it works for any pressure and any temperature, and uh… That's only for post-set form.
So we can turn parameters by focusing ionizations with. Uh, and if you change.
Larger than 14 EV, and promise you, I was 33%. But… it's not possible to…
Um, to apply this kind of… authenticating to CFO and small-sized mixtures.
Uh, there are some people working on this at CERN, and our previous PPD summer student.
It's also working on projects related to the simulation, uh. Mechanism in Survivor and local gas mixtures.
But it's not sold yet, so we only worked for a pure set forms.
And this is the result, uh, the memorable photons, uh, produced.
That could reach the anode. For each, uh, each diameter from 126 to 150.
So we'll assume that the first one was emitted right after the ionization, and to a random direction.
In the Popey Solar Handbook. And the personal distribution for 170 and 210 is not very much different.
The distribution is, uh, of the same shape, with only a slightly different standard deviation.
And then, uh, we moved on to the three exams. Because we want to maximize our game, and, uh, look at how.
How it's kind of helped. But the full length in the three times, because we have so many electrons, uh, produced.
Will take, uh, more than 10 hours, maybe 12 or maybe 15.
So we break the whole presence into steps. Um, first, we consider the trip to the first room, and then Avalanche in the gem pool, and then do it between gems.
And we repeat, uh. We repeat the process until we get to the maximum region.
And assimilated with Agile. So for the first step.
A drink to the Jam book, uh. It's just detection ritual of each spectrum. It's over 98%, which is very good, because.
Uh, we know that the gems are collecting electrons. Yeah.
And the second step, we need to think about. Uh, this… that these two general starting points.
Because, uh, different distributors are starting points will lead to different, uh, gain.
Because they feel slightly not come close to the glass wall, it will immediately attach to it.
And it was sadly, like, from, uh… far away from it, it will just start at lunch.
So we took a normal pace. That's not starting at a random position.
And in an extreme case. So, oh, you know, someone's like starting outocentric. So the difference between them is just within X percentage.
So, the one below is the random decision, and the one above is.
Outside of the fall center. And we did an additional check on asymmetry by choosing electrons starting.
Only at, uh, half of the… of the hope, at the top part.
Good. One half of the hope. And the symmetry will be washed out by Avalanche.
So no matter what distribution we get. At the top of the starting point.
Without always getting the same distribution. Uh, that's the person we're exiting hold.
And here is the… the drift between the poles. So, because we have a higher guilt in the transport rhythm.
We are going to see more electrons that attach, uh, to the cell phone in these regions.
And, uh… it helps us coming from, and we weren't what we would send to.
Um, one really main strength here, so we have… Uh, at least 61 hosts involved.
Uh, in the same conveyor.
And then the election reason. So, we've simulated, uh, the drip of neutrons exiting the final layer of plants.
And, uh, only 32.6%. Often, uh, get to the end of…
Which is the Edel mesh. And, uh, for each holiday, there's a little bit of a forward health maximum for.
440 microns. And it makes file with our… with our experience and with the simulations with other methods.
So this is… distribution as the ITO match, the annals. For Avalon, uh, privileged skin this way.
And, um, you can see that it shouldn't… it should be hidden.
As six-fold symmetric behavior, but that's it. It doesn't, because the asymmetry was lost out by the final Gaussian problem.
I've been in the natural region. So we checked again for aligned and misaligned champs.
So, for misalign times, we just, uh, uh… shift the gem to the X direction, like, half the pitch.
And, uh… It gives a slightly duller game, but only slightly lower.
But for the standard deviation of X and Y, the misaligned terms are certainly.
I also signed the adorn. So, um… We don't know which one is better. Uh, it's still pending on, uh, some checks with experimental results.
But, uh, this thing is quite high. So, it reminds us of cybernity issues, like sparkling and other stuff.
And also, we need to consider, uh… a fact called gain saturation.
By electrons shielding the field, uh, around another electron in the middle.
So, and also, we didn't consider any ions and the ion drip.
The highest. Something I'm still much more work to be done.
So the next step, uh. Well, the final assessment, I will be doing.
I will be generating, uh, Fulton Client Camera. Because, uh, similar to what I did for the.
For a single camera, and there can… some simulations for, say, for Argon mixture will be done, uh, once.
Thomas, the former TPD student, have figured out, uh. How electrons synthes therefore an outbound extreme.
And, uh, we can do that later in the team account builds.
Uh, somewhere. And I think with everything.
Uh, everything I have around.
Yeah. Thank you.
And so, is there? Any questions for Ruiza? Yes, please. Um, there was something you mentioned about electrons getting stuck at the bottom part of the germ, and that affecting your gain, please.
Yeah, because, uh…
Here, we have a uniform effect, of course. So, in these regions, naturally are, at maximum.
And because we are holding constant. Uh… constant voltage and.
This time and this thing, but not within poll, because it's just… And we have some… Uh, it actually feels nice pointing.
Pointing this way. That's pointing to the… in a public place here.
So, it's not directly pointing downwards, so you have points produced here.
Will be just immediately. Let's send it this way, and attached to the… to the commentator below. So, there are also some electrons.
So I'm not constricting downwards, and then catch by the actual field here.
And, uh, we tried to solve this issue by adding a kind of.
Etched ring. Well, they're both in there, or the bottom copper plate.
If it didn't help because. Uh, I think it's because of the tenant method not working for extremely high field.
What's the build? It's high-end. Also, it experienced a very adrun change at the corner.
So, uh… It's… it's a problem that we can't solve.
Using this simulation process. What questions? What is it?
I don't see any. One… on Zoom?
In that case, let's 10 quiz again.
So, our last speaker is Baboof. Uh, he's a student under the University of Oxford, and, uh, going to… going into his.
Especially mathematical and theoretical physics. That's kind of interest in energy physics and the use of machine learning in physics.
With the past 8 weeks, he has had the opportunity to work on a project with AI.
That combines both of his interstretchary areas. Thank you. Uh, okay.
Uh, so, yeah, uh, like Jothi said, for the past 8 weeks, I've been working on a project which, uh.
Combines machine learning and physics, specifically, I'm trying to, uh, reconstruct electron and photon energies.
In CMS captain data with the machine learning. Uh, so just quickly going over what scouting data is.
Uh, so in the CMS, we have… to effective blocks which process data.
Uh, there's the low-level triggers all the kind of custom low-level help hardware.
And then the high-level trigger, which is the software that serves the GPUs and CPUs.
And it outputs, um, events. And each event consists of a bunch of detector information.
And they're quite large. So, in the normal kind of CMS data, uh, single event might be the size of.
I'm gonna go ahead someday. Uh, so staffing dates effectively cuts down a lot of these extra.
Tracker and, uh, detect your information. To, uh, have the data which is less accurate.
But in exchange with demorbits. So, effectively creating a precision with more data.
Uh, so, yeah, the aim is to reduce uncertainty in the electron prototypon energies.
And we see in a scalping data, so this high-level trigger over here already makes an initial prediction for the energy of the electrons and photons.
And the aim of this project is to see if we can produce it in such incontinence.
Okay. Uh, so, firstly, uh, we generate some electrons, uh. For uniform dissembling and PT in the range of 0 to 300.
Uh, it's a suitability, sampled that between minus 2.5 and 2… uh, minus 2.5 and 2.5.
And provides just as a local angle, so that's maybe between, uh, minus pi and pi.
Once we sample an electron, which is basically a table of these three quantities.
Uh, we pass that into the protector. Similar simulator effects, and see what… We get out what we're given an electron D3 quantities.
Uh, so from that… We can, uh, plot… we can make an error plot.
Of the reconstruction. Versus the country. So, okay.
Uh, in this slots, well, the first one, CE Rico. It's this kind of initial energy that the high-level trigger predicts.
An electron or Putin will have. And it's over here, this is kind of like a signature that we'll take in.
Of the reconstructed energy from the HLT. In comparison to the Monte Carlo simulation.
Uh, so this is a more appropriate quantity to take, as opposed to just, say, delta E by itself, because… electrons at low energies, and electrons at high energies are going to have a different spread associated with them.
Uh, so it's kind of way to normalize, uh, the energy scale a little bit, so… Uh, one site or the other site doesn't completely screw the MR predictions. But, uh, but yeah.
It is called the three interesting features. So the central one is this peak over here, where the uncertainty switches is zero, which is kind of what we want.
Might be expecting a Gaussian peak, which is the case on both sides. Uh, however, you have this kind of shoulder region overhead.
Pill. Uh, but it deviates away from this kind of standard gashing behavior.
Uh, and finally, this sort of, kind of, decaying all the way down.
The peak… another peak is observed at the very end. Uh, so in the machine learning framework, well, we want to do is have sufficiently.
Ghost electrons, which still have a crazy high error, to further reconstruct them and make them better.
Certain reconstructions over here are not possible because of the folks I'm going to get to in a second.
And we want to have a way of filtering those out, because.
Predictions. We don't want to train on electrons that can't be reconstruct it. We want something that's reconstructible, and to make it better.
Uh, so, yeah, let's dive into what each of these, uh, features… represents. Uh, so initially, you might think that the error of scales of energy.
So, the poll on the left-hand side. Choose how the percentage error.
Lcu, but EMC, scales of Monte Carlo MSU. And it's interesting to see that the energy is already having much fun effect.
So, there might be some sort of deviations at higher energies from the low energy behavior.
The… this kind of large error persists, even when the good energies of.
700 a year left from POTS. So the more important of plots is this spot on the right.
Uh, which involves this E5 by 55 upon state. Uh, that's the one is C555?
Okay, so in the CMS, uh, the… two kind of detectives I'm primarily interested in is the interpreter.
And a crystal-based detector known as the electronic calorimeter. So that's the eco.
Uh, so in the ECAL, you have sets of 5x5 crystals, where you have the.
Empty of the electron protons being deposited. And then, uh… This wants to hit E555.
Quantifies how much energy is in this kind of 5x5 crystal matrix. So you see, uh, this entire thing basically represents more correction, what percentage.
Uh, of, uh, energy I have for my 5x5 crystal in comparison to my ground trip energy.
So when that fraction is very low, i.e, my 5x5 crystal or crystal bridge contains this little tuna.
Uh, energy. The error is going to be very high. And there's a sort of linear increase overhead that you see, which is associated with the shoulder. It shows a second ago.
Uh, on the plots, and then over here, you get discussion behavior.
Uh, and the variables in this person of the test region over here, which comes from other sector effects, which we'll get to in a second.
And it's good to remove these because, again, they will skew the machine learning predictions during training.
Uh, so… Uh, yeah. The tuna was the effects.
That's actually come true to all the two main factors I found are supermodel transitions and their crystals.
So yeah, what are supermodules? So, we have this 5x5 root of crystals in the CMS ETAB.
And this is fully collected into a bigger object, known as a supermodule.
And vehicles sucked around in the barrel from the end caps.
But in the transition regions between Supermajor 1, Super Module 2, for example.
There was a slight gap, and if electrons or photons go to that gap, energy will be lost, and as a result, reconstruction is going to be a lot harder.
So that suit modules. Uh… Part number two, or effect number two, comes from dead crystals, so this is from a crystal has, for example, glue opaque, and it can't produce any.
Electrical signal, which is the thermal detectors. All the electrical detectors themselves behind the crystal might not be working.
Uh, so that's essentially… presents itself as an empty region on a eater bipart fish.
Cons of emptying. There's a nice spot I can show at the end, which… have this little bit better. But for the time being, if we take our data.
Which is new clearly stores a second ago. And we removed the supermodule, all electrons and protons in the supermodules.
You see that you get rid of the shoulder overhead. So it goes away.
And then, third one, if you, uh, remove the crystals, or regions of the lab crystals.
You get rid of a lot of the bad data in this region over here.
Uh, combining these two, you get a plot that looks like this.
And then finally, we can also apply, kind of, the hard cutoff.
All on, uh, this era of UBA. So, again, the end goal is to get sufficiently well reconstructed electrons to reconstruct them better.
Uh, as opposed to trying to save and salvage something that's a… which might not even be possible.
Uh, so for that, I use the two different approaches. I tried two different models.
Uh, so the first one was a BB2. They boost the decision tree, and I believe the kind of initial prediction I'm getting from the high-level trigger.
Also involves the BDT somewhere in that pipeline. Uh, and the second one was a neural network.
So first, let's talk about the input feature we might call input features we might have.
Uh, so, uh, firstly, we have the PT eaten vibe, which is kind of already given to us by the HLC, which is kind of an initial prediction.
On what these quantities should do. We have tried the laser.
Uh, from the perspective, and also with each reading, we have an associated, kind of, 5x5 grid of crystals.
Uh, support BDC, you can only take the tabular features, you can't process things like images.
So what you have to do is take this 5x5 grid.
Both of which have been something which is hopefully meaningful to if you can see it.
So there's some input features that are on the next slide, which I'll show.
Uh, but yeah, it's essentially just taking widget sums of, uh.
This would overhead and ratios. To tell the model what the shell shape in this crystal lid looks like, and what kind of energies it contains.
An alternative to the privilege that is appealing is a neural network, because with a CNN.
A convolution neural network, you can directly process images. So you skip this step entirely, where you kind of have to handcraft the variables you want to put into your.
And third, you get a neural network predatory limit voice. Uh, so, yeah, in terms of the BDC, we have 3 input features.
That's… this is a little bit ahead. And we're predicting the corrections back then. So we have an interesting guest on the energy, which is Eureka.
And we're predicting attractive factor 2. Reduced obscene. Uh…
In contrast, for the neural network, uh, we're predicting the energy itself.
So, we'll get an internship features and the 555 Crystal brand.
So we're predicting that in June. So, uh, here's a quick example. I don't want to go too far into it, of some of the features that could come in, so…
Uh, on the left-hand side, this feature over here, sigma eta eta. So again, if you look at the equation at the bottom, it comes from a weighted sum.
On the 5x5 crystal, so this data comes from the eCamp.
And on the other hand, the data on the right-hand side comes from the tracker.
So, two kind of separate sets of, uh, variables and where they come from.
Uh, okay. So, starting with the BDC. Again, the target or the thing that we're predicting, is the corrective factor, which is why true.
And that's because we want to minimize, in terms of the loss function, again, we want to minimize this delta P by E.
And if you rewrite the equation, you can get the loss function which has come down over here.
As the ratio of correct applied to possible. Corrective fact is minus 1, and then squaring everything.
Uh, it's the ability to training looks like this. So, over here, you have the validation code, and over here you have the trade code, so… After such a number of trees in the BET, he begins to eclipse. However, the oath better than this kind of router baseline overhead, which is the baseline reconstruction of.
Hlt. Uh, so because a BBT on this case, HGBoost specifically.
Is a machine learning model. It's got some associated hyperparameters with it. Uh, some common ways to pick up hyperparameters is doing a grid search, or just kind of randomly selecting them, seeing what works best.
In this case, we used something named as Optuna, which is a… Uh, package, which kind of.
Automatically chose to select the best head premise for you. And you can get some nice spots as well, so over here, for example.
Uh, this package is picking up points. And that's a… yeah, maybe since we spoke with you.
So, the loss of a given trial, or a certified parameters that we've tried.
Uh, is Potter's as a kind of keep map. And you see where losses are high.
It doesn't like the sample points yet. Uh, whereas in the regions that are solo, it's much more likely sample points in this region.
So, maybe a kind of premise of bread search would search this entire space, which will tune, what it does, it says, okay, this is a high regional boss, I'm not going to search it, instead I'm going to focus my searches somewhere else.
Uh, so that was used to optimize the hyperparameters. And in terms of actual results, uh, this is what we got in the end.
So the result on the left-hand side is the correction of the barrel, and on the right-hand side is the correction on the end.
So, although there is a significant improvement, so you can see.
Uh, this community is what we had before, and shifted closer towards, uh, one.
Which is what we want. One thing to mention as well, it's not error anymore, it's… EMC, the button might be good for an LT.
So we won the first one, as opposed to serum. And for the MCAP, uh, we see significantly more points in the central region overhead.
So there's a massive, uh, massive difference in terms of, uh.
Reconstructed and corrected electron slash photon energies. Uh, so the next one, uh, the next model I tried was a deep loan, uh, CNN-based architecture.
Uh, so essentially what we're doing is we're taking these tabular features, which the BNC also had access to.
We're passing it through an LLP, most of it is acceptron.
And that's supposed to kind of embed the information. Of these features that have been into a higher dimensional space.
The same is done with the image, so CNN is also supposed to embed this information into a higher dimensional vector.
To all squeezed together, and then we're passing it through, and the MLP again to make a prediction.
So if you want the first part over here as kind of an encoder.
Of your information into a high-dimensional space. This, however, has a decoder back to entry provisions.
Um, so… this was a validation… this was the validation code, or the training code.
Uh, for the neural network. Uh, so it's significantly more spiky, or fluctuating more in comparison to BTC.
Uh, and it's also just barely beating the baseline at points.
Uh, upgrading the content in a second. Uh, but another interesting feature is how closely the curves follow each other.
Uh, which kind of indicates the two datasets. Uh, there's been training big enough.
Uh, for… Uh, they're large enough so that the two basically come from the same distribution.
Uh, so the problem most likely lies in the model. Uh, which, uh, oh yeah, another quick thing to mention is for deep learning, GPUs obviously, uh.
Top of a massive speedup, as opposed to CPUs. Uh, so yeah, almost certainly… it wasn't highly optimized for CPUs, but for most settings, out of the box, GPUs performed about 4 to 6 times faster than.
Pretty much all the sleeping settings right. For the decoding network.
Uh, so these are the end results of the deep learning network.
Uh, so in the MCAP, specifically, uh, the results are slightly better. However, in the barrel, the results are.
Whoops. Uh, so the neural network doesn't actually do a great job.
Uh, reconstructed, you know. The stuff in the barrel.
Uh, which is surprising in a sense, because it has access to all the same information as the, uh, as the DVC, just that it has the entire image, so it even has more.
But if it's not able to kind of pick out the correct combinations of this thing in that image.
Then maybe this high chocolate features end up outperforming, uh, whatever the neural network curve created.
Uh, so just to summarize, uh, here is a set of some results for the BBT.
Uh, in different PT ranges. So you see there's a massive improvement for the Linus PC range, and there's a big difference between the eCal reconstruction and TriPress ECAL reconstruction.
So, at the bottom blue curve represents the Reconstruction only using the calorimeter data on the BT.
The red one incorporates traffic data. So, that's a massive difference, because… Uh, the trackers has performed better at liver duties.
Uh, whereas the high-end PC, uh, the more similarity kind of possible, but the score is a little bit… a noticeable difference over.
The e-read that is, uh, that's given to us. Uh, so, just to summarize…
Uh, I analyzed, uh… and use ML to reduce their substitutional scouting data.
As we saw on the plot before, uh, the largest reduction in uncertainties observed for electrons in the fire to 20, uh.
You get electron volts at transit momentum in. However, throughout all of the things, all of these reconstructions are better than what we currently have.
Uh… some things in the Kosovs, uh, the electrons and photons that were incident on the supermodel caps might need to be addressed separately.
Just because it's different behavior, as opposed to in the middle of the crystal.
And the CNN could be further optimized as well. So the CNN has a long practice that could be, uh.
And it's also a lot more, kind of, uh. Susceptible to small changes.
Uh, there's a pretty wide space for scoring that can be done hence further.
Uh, and yeah, I think that's everything. Thanks for listening.
Pushing for buckled. What's in the room?
Why does the ECOT design the 5x5 crystal? Sorry? See you one more time. Why is the e-card design?
Of CMSE cut these eyes are 5x5. A Crystal metrics.
Um, so I'm… yeah, good question. I'm not… Uh, yeah, I'm not entirely sure. Probably due to the… more due to the practical constraints of wiring and, uh…
Like, the… the boxes need to be designed to a certain size, and the crystals are incredibly heavy, because there are not legit times to crystals. Yeah, we have one in the… But I don't know why it's… It's not… I don't think it's superficial. Probably just a combination of the actors and mechanics. Yeah.
Exactly, it does contain a large amount of CNG. Yeah, yeah, just a structural decision at that point.
More questions? This is Skinner. Do you know if your online electrons here have any sort of mechanism to do Romestralum energy recovery?
Uh, yes, one of the features that's available on the tracker is the amount of banks problem.
Firmly believe. And, uh, yeah, that goes into the model as well.
I think, uh, the… live reconstructed electrons only use ECAL data.
Uh, but the electrons reconstructed in some of these records over here.
Do involve, uh, the Brent's wrong, so… Just maybe another thing that would be nice… so at the moment, this is all in Monte Carlo compared to a troop electron.
Yeah, sure. You could also try comparing the online electron to the offline.
Reconstructed electron with sort of the best. Available modern reconstruction technology.
And then you could cross-reference your Monte Carlo results, also with data results, where you have.
Scouting event and the non-scouting event, the regular readout. Yeah.
See, make sure that what you've measured in Monte Carlo also carries over that amount of data. Yeah, that's right.
Yeah, that's something we can do in the future. But just look, just looking at these parts for the media team seems to be sort of performing more reasonably. Yeah. Um…
There seems to be that the peak is slightly offset in 1.
Does it spot the use of something you want to be worried about? Uh, yeah, so I think a big part of it is actually the loss function that we're using.
Uh, so again, in ML, the loss function basically comes from the high probability of assigning to each of these events.
So the fact that we're, for instance, optimizing delta U over E as opposed to just delta E.
Indicates other variance, scales of it. Uh, which could be broadly true.
Well, I know the policy's crystal ball functions to model some of these detector response distributions.
Uh, so this is a more primitive approach. In the future, if.
You're kind of able to… construct a loss function from one of these crystal ball functions, which would have more parameters.
Maybe these… also have been shifted over. So, for instance, maybe the left-handed distribution is always going to be a bit heavier than the right table.
It's not captured by the current loss function, it treats them.
On equal percent. So yeah, that's something you can do in the future, but it's going to take a bit more time.
Mentioned the cheap payments, which is GPUT, which is a round one for US commercials. Uh, yeah, thank you for all.
That's the heavy sheet mission. Yeah, the good GPU on the monthly norms.
But even if BDT is not significantly faster than just CPU usage. Again, this kind of best practices, but… Yeah, on deep neural networks, GPUs, all software speed up.
One question about those? I don't see anything on Zoom, and let's type in again.
So, any general questions for all the speakers from my lady? Anybody wants to ask?
Who have good experiences with anything before doing it.
Seems not. Then another round of uploads for all the speakers.
So, we'd be in the body of construction in 10 minutes and heading for lunch.
I really thought you were joining. So please feel free to join us.
Thanks